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      },
      "source": [
        "from torchvision import datasets\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "import numpy as np\n",
        "data_folder = '~/data/FMNIST' # This can be any directory you want \n",
        "# to download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)\n",
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "\n",
            "\n"
          ],
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        },
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4iqXqYb14wL"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/255\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix] \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bH5VEys19M4",
        "outputId": "16e6bb05-b48f-4319-ee0e-0d6fdf77c7f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "def get_data(): \n",
        "    train = FMNISTDataset(tr_images, tr_targets) \n",
        "    trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
        "    return trn_dl\n",
        "from torch.optim import SGD\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = SGD(model.parameters(), lr=1e-2)\n",
        "    return model, loss_fn, optimizer\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    model.train()\n",
        "    # call your model like any python function on your batch of inputs\n",
        "    prediction = model(x)\n",
        "    # compute loss\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    # based on the forward pass in `model(x)` compute all the gradients of \n",
        "    # 'model.parameters()'\n",
        "    batch_loss.backward()\n",
        "    # apply new-weights = f(old-weights, old-weight-gradients) \n",
        "    # where \"f\" is the optimizer\n",
        "    optimizer.step()\n",
        "    # Flush memory for next batch of calculations\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "def accuracy(x, y, model):\n",
        "    model.eval()\n",
        "    # since there's no need for updating weights, we might \n",
        "    # as well not compute the gradients\n",
        "    with torch.no_grad():\n",
        "        # get the prediction matrix for a tensor of `x` images\n",
        "        prediction = model(x)\n",
        "    # compute if the location of maximum in each row coincides \n",
        "    # with ground truth\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()\n",
        "trn_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()\n",
        "losses, accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    epoch_losses, epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        epoch_losses.append(batch_loss)\n",
        "    epoch_loss = np.array(epoch_losses).mean()\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        epoch_accuracies.extend(is_correct)\n",
        "    epoch_accuracy = np.mean(epoch_accuracies)\n",
        "    losses.append(epoch_loss)\n",
        "    accuracies.append(epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tIuYppL2Bj9",
        "outputId": "075468ca-e9aa-4ddb-c7e5-22eee674e502",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        }
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.figure(figsize=(20,5))\n",
        "plt.subplot(121)\n",
        "plt.title('Loss value over increasing epochs')\n",
        "plt.plot(epochs, losses, label='Training Loss')\n",
        "plt.legend()\n",
        "plt.subplot(122)\n",
        "plt.title('Accuracy value over increasing epochs')\n",
        "plt.plot(epochs, accuracies, label='Training Accuracy')\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7facd08c46d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6YOA3y52QnM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}